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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
Pharmacogenomics: Identification of New Drug Targets01:29

Pharmacogenomics: Identification of New Drug Targets

Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...
Genomics02:02

Genomics

Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
Translation01:31

Translation

Lesson: Translation
Translation is the process of synthesizing proteins from the genetic information carried by messenger RNA (mRNA). Following transcription, it constitutes the final step in the expression of genes. This process is carried out by ribosomes, complexes of protein and specialized RNA molecules. Ribosomes, transfer RNA (tRNA), and other proteins produce a chain of amino acids—the polypeptide—as the end product of translation.
Translation Produces the Building Blocks of Life
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...

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Updated: Jun 27, 2026

In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila
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In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila

Published on: August 20, 2019

Decoding Gene-Disease Associations with Computational Methods: A Survey.

Yupeng Zhai, Mengjing Li, Lei Yu

    IEEE Transactions on Computational Biology and Bioinformatics
    |June 25, 2026
    PubMed
    Summary
    This summary is machine-generated.

    Artificial intelligence, including graph neural networks and large language models, is revolutionizing gene-disease association prediction. This survey offers a comprehensive overview of AI methods, data resources, and applications to accelerate biological discovery.

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    Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
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    Last Updated: Jun 27, 2026

    In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila
    06:41

    In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila

    Published on: August 20, 2019

    Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)
    11:35

    Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)

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    Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
    09:34

    Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

    Published on: April 4, 2018

    Area of Science:

    • Computational biology
    • Bioinformatics
    • Artificial intelligence in medicine

    Background:

    • Identifying gene-disease associations (GDAs) is crucial but challenging due to vast data and experimental limitations.
    • Computational approaches are essential for prioritizing GDAs and integrating omics data.
    • Advances in AI, particularly GNNs and LLMs, offer powerful tools for GDA prediction.

    Purpose of the Study:

    • To provide a unified and up-to-date overview of AI-driven GDA prediction.
    • To summarize data resources, review methodological developments, and discuss applications.
    • To highlight challenges and future directions in AI for GDA prediction.

    Main Methods:

    • Review of public data resources for genes, diseases, and biological information.
    • Analysis of methodological advancements from traditional network-based methods to deep learning, GNNs, and LLMs.
    • Discussion of AI applications in gene prioritization, drug repurposing, and clinical research.

    Main Results:

    • AI, especially GNNs and LLMs, enables richer biological representations and more accurate GDA prediction.
    • A comprehensive overview of current AI techniques and their practical impact is presented.
    • The survey integrates data resources, methods, and applications for robust GDA prediction.

    Conclusions:

    • AI techniques are transforming GDA prediction, accelerating biological discovery.
    • This survey provides a foundation for developing more robust, interpretable, and clinically actionable computational tools.
    • Publicly available resources and literature are curated to support future research.